SOTAVerified

Graph Representation Learning

The goal of Graph Representation Learning is to construct a set of features (‘embeddings’) representing the structure of the graph and the data thereon. We can distinguish among Node-wise embeddings, representing each node of the graph, Edge-wise embeddings, representing each edge in the graph, and Graph-wise embeddings representing the graph as a whole.

Source: SIGN: Scalable Inception Graph Neural Networks

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 376–400 of 982 papers

TitleStatusHype
Heterogeneous Graph Contrastive Learning with Spectral Augmentation—0
Inference of Sequential Patterns for Neural Message Passing in Temporal Graphs—0
Harvesting Efficient On-Demand Order Pooling from Skilled Couriers: Enhancing Graph Representation Learning for Refining Real-time Many-to-One Assignments—0
Graph Representation Learning Strategies for Omics Data: A Case Study on Parkinson's Disease—0
Effective Edge-wise Representation Learning in Edge-Attributed Bipartite Graphs—0
RobGC: Towards Robust Graph Condensation—0
A Scalable and Effective Alternative to Graph Transformers—0
A Unified Graph Selective Prompt Learning for Graph Neural Networks—0
OLGA: One-cLass Graph AutoencoderCode0
Introducing Diminutive Causal Structure into Graph Representation Learning—0
Predicting Genetic Mutation from Whole Slide Images via Biomedical-Linguistic Knowledge Enhanced Multi-label ClassificationCode0
GEFL: Extended Filtration Learning for Graph ClassificationCode0
Enhancing Fairness in Unsupervised Graph Anomaly Detection through DisentanglementCode0
Know Your Neighborhood: General and Zero-Shot Capable Binary Function Search Powered by Call GraphletsCode0
Augmentation-based Unsupervised Cross-Domain Functional MRI Adaptation for Major Depressive Disorder Identification—0
Port-Hamiltonian Architectural Bias for Long-Range Propagation in Deep Graph Networks—0
Graphlets correct for the topological information missed by random walks—0
HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning—0
Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural NetworksCode0
Fair Graph Representation Learning via Sensitive Attribute DisentanglementCode0
Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural NetworksCode0
Relating-Up: Advancing Graph Neural Networks through Inter-Graph Relationships—0
AnchorGT: Efficient and Flexible Attention Architecture for Scalable Graph Transformers—0
Parameter-Efficient Tuning Large Language Models for Graph Representation Learning—0
Bounding the Expected Robustness of Graph Neural Networks Subject to Node Feature AttacksCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Pi-net-linearError (mm)0.47—Unverified